Keila Olaverria
Data Analyst with 12 years in finance & AI evaluation
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Transaction amount trends over time (credit card fraud)
Visualized how transaction amounts evolved over time to spot patterns that may signal periods of higher fraud risk.
Details / full description:
Plotted transaction amounts over time for both fraudulent and legitimate transactions.
Looked for spikes, clusters, and unusual periods in the time series.
Used this view to discuss how monitoring transaction streams can help flag emerging fraud activity earlier.
Demonstrated how simple visualizations support analysts and data scientists in designing better alerts.
Skills: Data analysis, Time series analysis, Data visualization
Tools: Python, Pandas, Matplotlib
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9
Credit card fraud detection analysis (Python)
Explored a credit card transactions dataset with Python to understand how fraudulent transactions differ from legitimate ones.
Details / full description:
Cleaned and prepared transaction data using pandas, focusing on key numerical features.
Visualized the distribution of fraudulent transaction amounts and compared them with normal transactions.
Calculated summary statistics (mean, median, ranges) to highlight unusual spending patterns.
Interpreted the results to show how this type of analysis can support fraud‑risk monitoring and model development.
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14